Lead AI and Data Solution Engineer

Responsibilities

• Solution Engineering: Lead the design, development, and deployment of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technical best practices.
• LLM and Agentic AI: Architect, implement, and optimize large language models and agentic AI workflows for business automation and decision support.
• Framework Expertise: Design and deploy AI solutions using leading frameworks such as LangChain, LangGraph, and n8n for scalable agent orchestration, workflow automation, and integration with business systems.
• MCP: Develop, integrate, and manage MCP-based solutions to enhance model interpretability, context management, and deployment at scale.
• Cloud and Data Engineering: Leverage AWS and Snowflake to build scalable, secure, and efficient data pipelines for structured and unstructured data.
• Collaboration: Partner with cross-functional teams, including other technology, business, risk, legal, and compliance stakeholders, to deliver integrated solutions.
• Innovation: Stay current with emerging technologies and industry trends in AI, data engineering, and cloud computing, driving continuous improvement and innovation.
• Governance and Compliance: Ensure all solutions meet regulatory, security, and compliance requirements relevant to the financial services industry.
• Mentorship: Provide technical leadership and mentorship to junior team members.

Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• Experience: 8+ years of experience in data engineering, AI solution development, or related roles.
• Skills: Proven expertise in large language models (LLMs), agentic AI systems, and Model Context Protocol (MCP); good working experience developing and integrating AI solutions using AWS Bedrock; strong experience with Snowflake and AWS services (Glue, S3, Lambda, SageMaker, etc.).
• Knowledge: Deep understanding of AI/ML frameworks, data pipelines, and cloud-native architectures; hands-on experience with LLM deployment, fine-tuning, and integration; proficiency in agentic AI design patterns and implementation; expertise in Model Context Protocol (MCP) for context-aware model deployment and management; strong knowledge of Snowflake, AWS, and advanced data modeling; experience with data governance, security, and compliance best practices.
• Other: Excellent communication, collaboration, and presentation skills; ability to translate complex technical concepts for non-technical stakeholders.

Pay

$200,000 to $215,000 U.S. FinTech's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) a candidate’s qualifications, skills, competencies, and experience, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.